There are existing researches devoted to invariant descriptions.
Most of these descriptions have a high computational cost and could not fit deep neural network structures.
The scale-invariant feature transform (SIFT) [1]} is the most widely used algorithm that generates rotation invariant descriptors for local patches in images.
For each located keypoint, the SIFT descriptor calculates the gradient distribution and decides the principal direction.
Then the algorithm rotates the principal direction to the x-axis, and the description vector was calculated.
The SIFT algorithm has been extensively investigated and applied widely.
There are a few adaptions of the original SIFT algorithm [2]}, [3]}, [4]} that extended the algorithm to 3D space.
Comparing to images, the calculation in 3D space is more complex due to the additional degree of freedom.
However, the chemical structure data under our consideration are discrete and sparse, and thus, the calculation could be significantly simplified.
We transform the coordinate system of the surrounding atoms according to Algorithm REF .
